Characterization and Modeling Using Non-Destructive Test (NDT) and Experimental Design Methods of a Self Compacting Concrete (SCC) Based on Mineral Additions
Bibliographic record
Abstract
The formulation of an innovative concrete that meets the requirements of a self-compacting concrete (SCC), with acceptable performance in terms of rheology in the fresh state; good fluidity, ease of placing, without segregation as well as good mechanical strength and durability at hardened state has become of great research interest for the last decades. Numerous studies have shown the favorable effects of limestone fillers on the SCC properties. This study aims at investigating the effect of inert mineral addition of limestone fillers with dosages of 10% and 20% grinded to different fine nesses 2000, 3000 and 4000 cm2/g on the physico-mechanical properties of a fresh self-placing concrete using slump, the L-box and the sieve stability tests. Also, the means of destructive and non-destructive tests (NDT) methods to the assessment of the mechanical performances of SCC at hardened state were used. The use of experiment factorial design method allows us to have behavior laws to predict the mechanical strength response when combined with (NDT) according to a numerical model in such study. Hence, a numerical modeling of mechanical response could be derived by such statistical analysis in regards to the effects of factors and their interaction. The results obtained showed that the incorporation of limestone filler in the composition of the SCC improves the fluidity with limited segregation, as well as the good mechanical performances (resistance to compression and flexion). The numerical modeling of the predicted compressive strength response, in particular at the age of 28 days, is judged to be with an acceptable determined coefficient R2 equal to 0.994.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".